City Research Online

Abstract

In this paper, we present an efficient in-DBMS framework for progressive time-aware sub-trajectory cluster analysis. In particular, we address two variants of the problem: (a) spatiotemporal sub-trajectory clustering and (b) index-based time-aware clustering at querying environment. Our approach for (a) relies on a two-phase process: a voting-and-segmentation phase followed by a sampling-and-clustering phase. Regarding (b), we organize data into partitions that correspond to groups of sub-trajectories, which are incrementally maintained in a hierarchical structure. Both approaches have been implemented in Hermes@PostgreSQL, a real Moving Object Database engine built on top of PostgreSQL, enabling users to perform progressive cluster analysis via simple SQL. The framework is also extended with a Visual Analytics (VA) tool to facilitate real world analysis.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Departments:	School of Science & Technology > Computer Science School of Science & Technology > Computer Science > giCentre

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